The paper introduces an Adaptive‑Horizon Graph Neural Operator (AH‑GNO) that learns to predict bed elevation changes directly on unstructured meshes by integrating area‑weighted graph learning, historical state selection, and forecast horizon estimation. Evaluated on Telemac2D/GAIA benchmarks and a field‑scale Waiho River case, the AH‑GNO accurately reproduces key morphodynamic patterns while drastically reducing bed‑update computation time. Deployment on CPU‑GPU platforms achieves speedups of 150–360× over 4‑core GAIA CPUs and 105–186× over 20‑core CPUs.
arXiv:2609.37565v1 Announce Type: new Abstract: Deep learning surrogates can produce high-resolution coastal flood maps orders of magnitude faster than physics-based hydrodynamic simulators, yet tran...
arXiv:2606. 11162v1 Announce Type: new Abstract: In this work, we present COGENT, a continuous graph emulator with Neural Ordinary Differential Equations for long-term physical forecasting on irregular geospatial meshes.
arXiv:2509.06154v3 Announce Type: replace Abstract: Developing accurate, data-efficient surrogate models is central to advancing AI for Science. Neural operators (NOs), which approximate mappings bet...
arXiv:2609.39005v1 Announce Type: new Abstract: Emergency managers need to know where floodwater is, how deep it is, and how it will change over the coming hours across an entire river basin. During...
The paper introduces a physics‑refined framework for spatiotemporal forecasting on open‑boundary hydrologic graphs, addressing instability caused by missing external boundary forcing. It learns ghost node proxies to approximate unobserved inputs and applies two physics refiners: one enforcing local consistency with two‑hop neighbors, and another using a physics‑guided graph neural operator to reduce long‑horizon drift. Experiments on two real‑world hydrologic graphs show improved prediction accuracy and stability compared to existing learning‑based and physics‑informed models.
arXiv:2606. 17180v1 Announce Type: new Abstract: This chapter discusses how a data-driven machine learning approach can reproduce key aspects of the physical behavior of multiphase flows in complex geological formations.
arXiv:2608. 16070v1 Announce Type: cross Abstract: Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning.
Emergency managers need to know where floodwater is, how deep it is, and how it will change over the coming hours across an entire river basin. During a flood, however, real-time measurements come fro...
Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning. Physics-based ocean forecasting models impose prohibitive computational costs,...
HClimRep‑Ocean is a machine‑learning emulator that operates directly on the native unstructured mesh of the FESOM2 ocean model, trained on a 209‑year AWI‑CM3 control run and run without atmospheric forcing except at initialization. It shows strong skill for current forecasts at 30‑day lead times, outperforming all references, while temperature and salinity forecasts are best served by a damped‑anomaly persistence approach. In independent OceanBench testing, a reanalysis‑trained variant achieves the lowest RMSE against GLORYS reanalysis, demonstrating the competitiveness of the native‑mesh approach.
arXiv:2608.29355v1 Announce Type: new Abstract: Large non-uniform point sets make direct attention-based surrogate modeling costly for ship hydrodynamics. We introduce APPSolver, a point-wise flow-pr...
arXiv:2608. 05132v1 Announce Type: cross Abstract: Predicting how a subcortical structure's shape will evolve from a few prior scans could support prognosis and clinical-trial enrichment.